Abstract
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R2 = 0.676, P<0.0001; linear regression modeling, R2 = 0.598, P<0.0001). Logarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
Highlights
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term [1]
Subjects Two rounds of data collection were performed in the prediction of cognitive recovery: the first was for the purpose of identifying the time course of cognitive recovery in the early phase of group data
The second round of data collection was for the purpose of ensuring that the correlation of the group data applied to the prediction of each individual’s degree of cognitive recovery and for predicting individual cognitive recovery by logarithmic and linear regression modeling based on the individual slope of the early phase of cognitive recovery
Summary
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term [1]. Koyama et al [16] and Suzuki et al [17] examined the validity and the applicability of logarithmic modeling for predicting the functional recovery of stroke patients with hemiplegia These studies noted that the time-course observation data plotted for each individual were closely related to the data change derived from the predictive model. For both service providers and receivers, accurate prediction enables effective use of resources by allowing better estimation of such factors as length of hospitalization [24] For both individual patients and health care administrators, accurate prediction of cognitive recovery would provide crucially important information
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